Context-Aware Routing Service for an Intelligent Mobile-Tourist Guide

Smirnov, Alexander; Teslya, Nikolay; Kashevnik, Alexey

doi:10.1007/978-3-319-59539-9_18

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…Therefore, dynamic information should be considered in navigation during the trip, not when creating the itinerary before the trip. In fact, many studies have described navigation using dynamic information [12]- [17]. For instance, study [13] examines a method that recommends the next tourist spot based on dynamic information such as congestion, weather, and events.…”

Section: Conventional Researchesmentioning

confidence: 99%

“…Actually applying them to tourism for buses or trains is difficult. Some studies have proposed navigation considering dynamic in- formation for various transportation [15]- [17]. Navigation for public transport must consider dynamic waiting times because they might lead to difficulties when a schedule change affects tourism related to public transportation.…”

Section: Conventional Researchesmentioning

confidence: 99%

“…Therefore, many related studies have been undertaken to investigate navigation during trips. Particularly, recent navigation often considers information that changes dynamically during the trip, such as weather and congestion [12]- [17]. As described in this paper, we define such information as dynamic information.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tourism Application Considering Waiting Time

SAITO

KIM

Manabe

2023

IEICE Trans. Fundamentals

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Currently, the proportion of independent travel is increasing in Japan. Therefore, earlier studies supporting itinerary planning have been presented. However, these studies have only insufficiently considered rural tourism. For example, tourist often use public transportation during trips in rural areas, although it is often difficult for a tourist to plan an itinerary for public transportation. Even if an itinerary can be planned, it will entail long waiting times at the station or bus stop. Nevertheless, earlier studies have only insufficiently considered these elements in itinerary planning. On the other hand, navigation is necessary in addition to itinerary creation. Particularly, recent navigation often considers dynamic information. During trips using public transportation, schedule changes are important dynamic information. For example, tourist arrive at bus stop earlier than planned. In such case, the waiting time will be longer than the waiting time included in the itinerary. In contrast, if a person is running behind schedule, a risk arises of missing bus. Nevertheless, earlier studies have only insufficiently considered these schedule changes. In this paper, we construct a tourism application that considers the waiting time to improve the tourism experience in rural areas. We define waiting time using static waiting time and dynamic waiting time. Static waiting time is waiting time that is included in the itinerary. Dynamic waiting time is the waiting time that is created by schedule changes during a trip. With this application, static waiting times is considered in the planning function. The dynamic waiting time is considered in the navigation function. To underscore the effectiveness of this application, experiments of the planning function and experiments of the navigation function is conducted in Tsuruoka City, Yamagata Prefecture. Based on the results, we confirmed that a tourist can readily plan a satisfactory itinerary using the planning function. Additionally, we confirmed that Navigation function can use waiting times effectively by suggesting additional tourist spots.

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Section: Conventional Researchesmentioning

confidence: 99%

Section: Conventional Researchesmentioning

confidence: 99%

See 1 more Smart Citation

Tourism Application Considering Waiting Time

SAITO

KIM

Manabe

2023

IEICE Trans. Fundamentals

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“…In this regard, user-related information can be considered part of the context. According to [26], context-related information is assigned into personal and environmental context. Figure 3 shows the context information.…”

Section: Context-related Informationmentioning

confidence: 99%

A Context-Aware Route Finding Algorithm for Self-Driving Tourists Using Ontology

et al. 2019

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This study proposed a context-aware ontology-based route finding algorithm for self-driving tourists. In this algorithm, two ontologies—namely drivers’ experiences and required tourist services—were used according to tourist requirements. Trips were classified into business and touristic. The algorithm was then compared with Google Maps in terms of travel time and travel length for evaluation. The results showed that the proposed algorithm performed similarly to Google Maps in some cases of business trips and better in other cases, with a maximum 10-min travel time difference. In touristic trips, the capabilities of the proposed algorithm were far better than those of Google Maps.

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Context-Aware Routing Service for an Intelligent Mobile-Tourist Guide

Cited by 2 publications

References 13 publications

Tourism Application Considering Waiting Time

Tourism Application Considering Waiting Time

A Context-Aware Route Finding Algorithm for Self-Driving Tourists Using Ontology

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